Madhavi Devaraj, Vasanthakumar Namasivayam, Satya Swarup Srichandan, Eshan Sharma, Apjit Kaur, Nibha Mishra, Dev Vimal Seth, Akanksha Singh, Pankaj Saxena, Eshaan Vasanthakumar, James Blanchard, Ravi Prakash
{"title":"开发和测试基于人工智能的移动应用程序，以实现印度北方邦无白内障积压状态。","authors":"Madhavi Devaraj, Vasanthakumar Namasivayam, Satya Swarup Srichandan, Eshan Sharma, Apjit Kaur, Nibha Mishra, Dev Vimal Seth, Akanksha Singh, Pankaj Saxena, Eshaan Vasanthakumar, James Blanchard, Ravi Prakash","doi":"10.1016/j.apjo.2024.100094","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Uttar Pradesh (UP), the most populous state in India, has about 36 million people aged 50 years or older, spread across more than 100,000 villages. Among them, an estimated 3.5 million suffer from visual impairments, including blindness due to untreated cataracts. To achieve cataract backlog-free status, UP is required to screen this population at the community level and provide treatment to those suffering from cataracts. We envisioned an AI-powered primary screening app utilizing eye images, deployable to frontline health workers for community-level screening. This paper outlines insights gained from developing the AI mobile app \"Roshni\" for cataract screening.</p><p><strong>Method: </strong>The AI-based cataract classification model was developed using 13,633 eye images and finalized after three stages of experiments, detecting cataracts in images focused on the eye, iris, and pupil. Overall, 155 experiments were conducted using multiple deep learning algorithms, including ResNet50, ResNet101, YOLOv5, EfficientNetV2, and InceptionV3. We established a minimum threshold of 90 % specificity and sensitivity to ensure the algorithm's suitability for field use.</p><p><strong>Results: </strong>The cataract detection model for eye-focused images achieved 51.9 % sensitivity and 87.6 % specificity, while the model for iris-focused images, using a good/bad iris filter, achieved 52.4 % sensitivity and 93.3 % specificity. The classification model for segmented-pupil images, employing a good/bad pupil filter with UNet-based semantic segmentation model and EfficientNetV2, yielded 96 % sensitivity and 97 % specificity. Field testing with 302 beneficiaries (604 images) showed an overall sensitivity of 86.6 %, specificity of 93.3 %, positive predictive value of 58.4 %, and negative predictive value of 98.5 %.</p><p><strong>Conclusion: </strong>This paper details the development of an AI mobile app designed to facilitate community screening for cataracts by frontline health workers.</p>","PeriodicalId":8594,"journal":{"name":"Asia-Pacific Journal of Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and testing of Artificial Intelligence based mobile application to achieve cataract backlog-free status in Uttar Pradesh, India.\",\"authors\":\"Madhavi Devaraj, Vasanthakumar Namasivayam, Satya Swarup Srichandan, Eshan Sharma, Apjit Kaur, Nibha Mishra, Dev Vimal Seth, Akanksha Singh, Pankaj Saxena, Eshaan Vasanthakumar, James Blanchard, Ravi Prakash\",\"doi\":\"10.1016/j.apjo.2024.100094\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Uttar Pradesh (UP), the most populous state in India, has about 36 million people aged 50 years or older, spread across more than 100,000 villages. Among them, an estimated 3.5 million suffer from visual impairments, including blindness due to untreated cataracts. To achieve cataract backlog-free status, UP is required to screen this population at the community level and provide treatment to those suffering from cataracts. We envisioned an AI-powered primary screening app utilizing eye images, deployable to frontline health workers for community-level screening. This paper outlines insights gained from developing the AI mobile app \\\"Roshni\\\" for cataract screening.</p><p><strong>Method: </strong>The AI-based cataract classification model was developed using 13,633 eye images and finalized after three stages of experiments, detecting cataracts in images focused on the eye, iris, and pupil. Overall, 155 experiments were conducted using multiple deep learning algorithms, including ResNet50, ResNet101, YOLOv5, EfficientNetV2, and InceptionV3. We established a minimum threshold of 90 % specificity and sensitivity to ensure the algorithm's suitability for field use.</p><p><strong>Results: </strong>The cataract detection model for eye-focused images achieved 51.9 % sensitivity and 87.6 % specificity, while the model for iris-focused images, using a good/bad iris filter, achieved 52.4 % sensitivity and 93.3 % specificity. The classification model for segmented-pupil images, employing a good/bad pupil filter with UNet-based semantic segmentation model and EfficientNetV2, yielded 96 % sensitivity and 97 % specificity. Field testing with 302 beneficiaries (604 images) showed an overall sensitivity of 86.6 %, specificity of 93.3 %, positive predictive value of 58.4 %, and negative predictive value of 98.5 %.</p><p><strong>Conclusion: </strong>This paper details the development of an AI mobile app designed to facilitate community screening for cataracts by frontline health workers.</p>\",\"PeriodicalId\":8594,\"journal\":{\"name\":\"Asia-Pacific Journal of Ophthalmology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Asia-Pacific Journal of Ophthalmology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.apjo.2024.100094\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asia-Pacific Journal of Ophthalmology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.apjo.2024.100094","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
Development and testing of Artificial Intelligence based mobile application to achieve cataract backlog-free status in Uttar Pradesh, India.
Background: Uttar Pradesh (UP), the most populous state in India, has about 36 million people aged 50 years or older, spread across more than 100,000 villages. Among them, an estimated 3.5 million suffer from visual impairments, including blindness due to untreated cataracts. To achieve cataract backlog-free status, UP is required to screen this population at the community level and provide treatment to those suffering from cataracts. We envisioned an AI-powered primary screening app utilizing eye images, deployable to frontline health workers for community-level screening. This paper outlines insights gained from developing the AI mobile app "Roshni" for cataract screening.
Method: The AI-based cataract classification model was developed using 13,633 eye images and finalized after three stages of experiments, detecting cataracts in images focused on the eye, iris, and pupil. Overall, 155 experiments were conducted using multiple deep learning algorithms, including ResNet50, ResNet101, YOLOv5, EfficientNetV2, and InceptionV3. We established a minimum threshold of 90 % specificity and sensitivity to ensure the algorithm's suitability for field use.
Results: The cataract detection model for eye-focused images achieved 51.9 % sensitivity and 87.6 % specificity, while the model for iris-focused images, using a good/bad iris filter, achieved 52.4 % sensitivity and 93.3 % specificity. The classification model for segmented-pupil images, employing a good/bad pupil filter with UNet-based semantic segmentation model and EfficientNetV2, yielded 96 % sensitivity and 97 % specificity. Field testing with 302 beneficiaries (604 images) showed an overall sensitivity of 86.6 %, specificity of 93.3 %, positive predictive value of 58.4 %, and negative predictive value of 98.5 %.
Conclusion: This paper details the development of an AI mobile app designed to facilitate community screening for cataracts by frontline health workers.
期刊介绍：
The Asia-Pacific Journal of Ophthalmology, a bimonthly, peer-reviewed online scientific publication, is an official publication of the Asia-Pacific Academy of Ophthalmology (APAO), a supranational organization which is committed to research, training, learning, publication and knowledge and skill transfers in ophthalmology and visual sciences. The Asia-Pacific Journal of Ophthalmology welcomes review articles on currently hot topics, original, previously unpublished manuscripts describing clinical investigations, clinical observations and clinically relevant laboratory investigations, as well as .perspectives containing personal viewpoints on topics with broad interests. Editorials are published by invitation only. Case reports are generally not considered. The Asia-Pacific Journal of Ophthalmology covers 16 subspecialties and is freely circulated among individual members of the APAO’s member societies, which amounts to a potential readership of over 50,000.
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